Agentic AI Engineer

Job Description

Senior Agentic AI Engineer

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Embedded Engineering Engagement

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We are hiring Senior Agentic AI Engineers to join a high-impact engineering team building the next generation of AI-driven software testing and developer workflows.

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This is not a traditional ML research or model-training role . We are looking for strong software and systems engineers who know how to build with AI agents: designing agentic workflows, integrating agents into existing engineering systems, managing context effectively, and making AI-driven automation reliable enough for real-world use.

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The ideal engineer is hands-on, highly autonomous, comfortable navigating a large codebase, and able to take an ambiguous problem from concept through implementation.

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What You'll Work On

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You may work across several closely related areas, including:

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• Agentic workflow integration — Integrating autonomous AI workflows into existing engineering and test infrastructure.
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• AI-driven test automation — Improving systems that generate, execute, analyze, and maintain automated tests.
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• Context management — Building systems that provide agents with the right code, history, tools, state, and information at the right time.
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• Developer-agent tooling — Improving how AI agents interact with repositories, build systems, test infrastructure, and engineering workflows.
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• Reliability and evaluation — Debugging agent behavior, identifying failure modes, and improving the consistency and usefulness of agent-generated results.
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Responsibilities

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• Design, build, and integrate production-oriented agentic AI workflows.
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• Develop tooling that enables AI agents to interact effectively with large codebases and engineering systems.
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• Improve automated testing and test-generation workflows using LLMs and coding agents.
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• Design context-management and retrieval strategies for long-running or complex agent workflows.
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• Debug failures across agents, tools, builds, tests, and surrounding infrastructure.
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• Build integrations between agentic systems and existing developer/test platforms.
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• Evaluate agent output and develop mechanisms to improve reliability and reduce failures or unnecessary human intervention.
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• Work directly with senior technical stakeholders to turn loosely defined problems into working systems.
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• Own projects end-to-end and communicate progress, risks, and blockers clearly.
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What We're Looking For

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Required

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• 5+ years of professional software engineering experience, or equivalent demonstrated depth.
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• Strong Python and/or comparable systems/backend programming experience.
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• Strong Linux and software-debugging fundamentals.
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• Experience building or integrating LLM-powered applications, coding agents, or agentic workflows.
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• Experience with APIs, developer tooling, automation, test infrastructure, CI/CD, or large software systems.
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• Ability to understand and modify unfamiliar codebases quickly.
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• Strong engineering judgment around reliability, observability, testing, and maintainability.
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• Excellent written and verbal communication.
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• Demonstrated ability to independently drive ambiguous technical work.
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Particularly Valuable Experience

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• Claude Code, Claude SDK/API, or similar coding-agent platforms.
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• Agent orchestration, tool use/function calling, MCP, skills, subagents, or multi-step agent workflows.
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• Context engineering, context management, retrieval, memory, or prompt/tool architecture.
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• Automated testing frameworks or AI-generated testing.
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• Large-scale developer infrastructure or internal engineering platforms.
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• Android, wearables, AR/VR, or other device-oriented development environments.
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• CI/CD, build systems, containers, telemetry, or production debugging.
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• Experience embedding AI capabilities into an established engineering ecosystem rather than building standalone demos.
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Deep ML, computer vision, and model-training expertise are not required.

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The Engineer Who Will Thrive Here

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We are looking for someone who combines strong engineering fundamentals with an AI-native way of working .

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You should be comfortable receiving a problem rather than a detailed implementation plan, investigating the surrounding system, collaborating with stakeholders, and driving toward a working solution.

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You communicate proactively, surface blockers early, document important decisions, and maintain visibility while working independently. This is a high-autonomy environment where ownership matters as much as technical ability.

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Example Problems

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You might be asked to:

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• Integrate an autonomous testing agent with an existing test platform.
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• Improve an agent's ability to understand and navigate a large repository.
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• Build context-management infrastructure for multi-step agent workflows.
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• Automate portions of test creation, execution, debugging, or maintenance.
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• Determine why an agent succeeds on some workflows but fails unpredictably on others.
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• Design tooling that lets an agent safely interact with existing developer infrastructure.
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• Reduce the amount of human intervention required to complete complex engineering workflows.
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Engagement

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Long-term consulting engagement working directly with engineers at a major technology company.

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Remote , with substantial collaboration across engineering teams.